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Pay Range: $53.00hr - $58.00hr Job Overview: Our client is seeking a highly motivated hands-on DevOps Lead Engineer to support and enhance mission-critical payment platforms in a 24x7 global environment. The ideal candidate will demonstrate strong technical expertise, leadership, ownership, responsiveness, and communication skills while driving platform reliability, automation, operational excellence, and continuous improvement. Mandatory Skills: Kubernetes, Helm, Docker. Terraform. GitHub, Concourse CI/CD. Oracle, SQL, PL/SQL. AWS, Cloud Foundry. AppDynamics, Kibana, Client , or similar monitoring tools. Linux/Unix Administration. Python, Shell Scripting. Agile/Scrum and Jira. Production Support, Incident Management, and Automation. Optional Skills: Payments, Financial Services, or Billing domain. Large-scale, high-availability production environments. Experience with AI/GenAI tools such as GitHub Copilot, ChatGPT, Claude, or Gemini. Key Competencies: Leadership, Ownership, and Accountability. Strong Communication and Stakeholder Management. Customer Focus, Responsiveness, and Sense of Urgency. Incident Leadership and Problem Solving. Offshore Team Coordination. Continuous Improvement and Automation Mindset. AI Awareness and Innovation. Key Responsibilities: Lead production support, incident triage, root cause analysis, and resolution of critical payment applications. Provide timely response to production issues and lead major incident bridge calls through closure. Participate in and coordinate on-call rotations to ensure continuous service availability. Collaborate with offshore and cross-functional teams to ensure seamless support, issue resolution, and knowledge transfer. Manage deployments, CI/CD pipelines, monitoring, and operational processes. Drive automation, reliability, and continuous improvement initiatives. Define and track operational KPIs, SLAs, and service health metrics. Mentor team members and promote operational best practices. Leverage AI-powered tools to improve troubleshooting, productivity, and operational efficiency. Other duties and responsibilities as assigned. Qualifications: The successful candidate will be a proactive technical leader who takes ownership, responds with urgency, collaborates effectively across global teams, and leverages automation and AI to deliver reliable, scalable, and resilient payment solutions. Bachelor's degree in Computer Science, Engineering, or a related field. 8+ years of experience in DevOps, Production Support, SRE, or Platform Engineering. Experience leading incident response, on-call support models, and globally distributed teams. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. The Principal AI Engineer provides the highest level of individual-contributor technical leadership for the company's AI platform, operating one level above the Lead AI Engineer role. This person sets the multi-year architecture strategy for agentic and LLM-powered systems - spanning model strategy, retrieval-augmented generation (RAG), tool/connector ecosystems, and evaluation/governance tooling - across every team that builds on the AI platform, not just one. In a highly regulated healthcare environment, the Principal AI Engineer resolves the hardest, most ambiguous technical problems facing the platform, is a primary technical voice with executive leadership on AI strategy and risk, and shapes how the entire engineering organization adopts AI safely and effectively. Success is measured by the durability and adoption of the technical strategy this role sets across multiple teams, this role's ability to unblock cross-organizational technical risk before it becomes a delivery or compliance problem, and its influence on AI investment decisions at the executive level. Key Responsibilities Enterprise AI Strategy: Own the multi-year technical strategy and roadmap for the AI platform across all teams building on it, not a single product or team - model strategy, RAG/retrieval architecture, connector ecosystems, and evaluation frameworks. Technical Authority: Serve as the final technical escalation point for the hardest, most ambiguous architecture and reliability problems on the AI platform, spanning multiple teams and systems. Cross-Team Standards: Set architecture, security, and evaluation standards that multiple Lead AI Engineers and their teams adopt; audit and evolve those standards as the platform and threat landscape change. Executive Advisory: Serve as a primary technical voice to VP- and executive-level leadership on AI platform strategy, model risk, and investment trade-offs. Deep Technical Delivery: Personally lead architecture and prototyping for the organization's highest-ambiguity, highest-risk AI initiatives - the problems no existing team or pattern already solves. Mentorship at Scale: Develop Lead and Senior AI Engineers into stronger technical leaders through design review, technical coaching, and structured knowledge-sharing across teams. Responsible AI & Governance: Shape enterprise-wide AI governance policy - model risk review, connector/tool approval frameworks, and PHI-handling standards - in partnership with Legal/Compliance/Privacy leadership. Vendor & Model Strategy at Scale: Own evaluation and long-term strategy for foundation model providers and AI infrastructure vendors across the company, not just a single team's stack. External Technical Representation: Represent the company's AI engineering capability externally - industry forums, partner/vendor technical relationships, and recruiting for senior technical talent. Organizational Impact: Influence AI platform direction and investment across the broader enterprise, extending beyond a single business unit. Priority will be given to candidates who reside in the Columbus, OH metropolitan area. We are unable to provide sponsorship for this role, presently or in the future. Minimum Requirements Bachelor's degree or equivalent experience; typically 12+ years of software engineering experience, including 6+ years focused on AI/ML, LLM, or NLP systems in production and 3+ years operating as a senior individual-contributor technical leader. Fewer years required with a relevant Master's or Doctorate. Critical Skills One level of demonstrated technical leadership above a Lead AI Engineer - e.g., setting technical direction and standards across multiple teams, not just owning architecture for a single team or platform area. Proven track record setting technical strategy that multiple teams adopt, not just delivering within one team's roadmap. Deep, hands-on expertise building production LLM applications at scale: prompt engineering, RAG, agentic workflows, tool use, and evaluation harnesses - with the judgment to know which patterns will and won't hold up over multiple years. Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP), including evaluating emerging alternatives. Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable). Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale across multiple products. Deep working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines, including designing these systems from scratch. Demonstrated ability to influence and align senior engineering and executive stakeholders without direct authority. Deep familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling). Preferred Qualifications Advanced degree (M.S. or Ph.D.) in Computer Science, AI/ML, or a related technical field. Prior experience as a Principal, Staff, or Distinguished Engineer setting AI/ML technical strategy across multiple teams inside healthcare IT or another highly regulated industry. Track record of publications, patents, open-source contributions, or industry speaking on applied AI/LLM systems. Experience shaping enterprise-wide AI governance policy in partnership with Legal, Compliance, and Privacy leadership. Additional Skills Deep awareness of the fast-moving GenAI/agentic landscape and a demonstrated ability to separate durable technical bets from hype. Experience managing strategic relationships with AI infrastructure vendors and model providers at the enterprise level. Exceptional written and verbal communication for executive, board-level, and external technical audiences. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $195,000 - $325,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. About the Team Our AI Enablement team drives enterprise-wide adoption of generative AI, spanning platform engineering, governance, and hands-on delivery of AI-powered products and internal tooling. The team builds and operates the infrastructure, integrations, and knowledge systems that let engineering and business teams safely adopt large language models at scale. The Lead AI Engineer joins as the senior technical authority on this team, setting the architectural direction that Senior and Associate AI Engineers build against. Position Summary The Lead AI Engineer provides technical leadership and architecture ownership for the company's AI platform and its embedded delivery initiatives, operating one level above the Senior AI Engineer role. This person owns the technical design of agentic and LLM-powered systems built on large language model APIs, including retrieval-augmented generation (RAG), tool/connector integration, and evaluation/governance tooling, in a highly regulated healthcare environment. The Lead AI Engineer sets architectural standards across AI-embedded product delivery, mentors Senior and Associate AI Engineers, and represents AI platform architecture to engineering leadership and Legal/Compliance/Privacy. Success is measured by the reliability, security, and scalability of the AI systems shipped, the rate at which Senior/Associate engineers adopt the architectural standards this role sets, and this role's influence on AI platform decisions across the organization. Key Responsibilities AI Architecture Leadership: Define the technical architecture and roadmap for LLM/agentic systems - model routing across cloud AI platforms, connector/integration patterns, RAG pipelines, and prompt/evaluation frameworks. Solution Design & Governance: Lead architecture reviews for new AI capabilities and ensure designs meet enterprise security, privacy, and data-provenance requirements. Platform Ownership: Drive the technical design of shared AI platform capabilities, including knowledge-retrieval systems, citation/traceability of AI-generated outputs, and permission-aware access to data. Hands-On Delivery: Personally architect and build high-complexity components on priority AI delivery initiatives where a working system, not just a design doc, is the deliverable. Technical Mentorship: Set coding, architecture, and evaluation standards for Senior and Associate AI Engineers; run design reviews and pair on complex agentic workflows. LLMOps & Responsible AI: Establish secure, observable, and cost-attributed AI operations - usage/spend attribution, abuse monitoring, model evaluation, and rollback strategy - for production LLM systems. Cross-Functional Architecture: Partner with engineering managers, product, platform, and Legal/Compliance to align AI architecture decisions with delivery timelines and enterprise standards. Vendor & Model Strategy: Evaluate foundation model providers and AI tooling vendors; make build-vs-buy and model-selection recommendations. Compliance & Risk: Ensure AI systems handling protected health information (PHI) meet regulatory and enterprise governance requirements. Organizational Impact: Represent AI architecture in cross-company forums and influence enterprise-wide AI platform direction. Priority will be given to candidates who reside in the Columbus, OH metropolitan area. We are unable to provide sponsorship for this role, presently or in the future. Minimum Requirements Bachelor's degree or equivalent experience; typically 8+ years of software engineering experience, including 3+ years focused on AI/ML, LLM, or NLP systems in production. Fewer years required with a relevant Master's or Doctorate. Critical Skills One level of demonstrated technical leadership above a Senior AI Engineer - e.g., owning architecture for a team or platform area, not just a single feature. Deep, hands-on expertise building production LLM applications: prompt engineering, RAG/GraphRAG, agentic workflows, tool use, and evaluation harnesses. Experience with commercial large language model APIs (e.g., Anthropic, OpenAI) and tool-integration protocols such as Model Context Protocol (MCP). Strong software engineering fundamentals: Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design (Azure preferred; AWS/GCP acceptable). Experience with vector/graph retrieval systems, embeddings, and knowledge-base architectures at production scale. Working knowledge of LLMOps/MLOps: usage attribution, cost/spend monitoring, abuse detection, and model evaluation pipelines. Ability to set architectural standards and mentor other engineers through design review and pairing, not just documentation. Familiarity with regulated/healthcare data environments and AI governance frameworks (model risk review, connector approval tiers, PHI handling) is a plus. Preferred Qualifications Advanced degree (M.S. or higher) in Computer Science, AI/ML, or a related technical field. Prior experience leading AI/ML platform or applied-AI teams inside healthcare IT or another highly regulated industry. Experience modernizing legacy systems toward AI-embedded workflows (agent-assisted development, AI-powered risk scoring, AI-driven automation). Experience with Confluence/Jira-based engineering governance and enterprise architecture review boards. Additional Skills Strong awareness of the fast-moving GenAI/agentic landscape and a demonstrated habit of continuous learning. Experience managing relationships with AI tooling vendors and model providers. Clear written and verbal communication for both technical and executive audiences We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $155,300 - $258,800 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Outcomes and Activities: Own the deployment path for ML and GenAI models end to end training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA and production. Own runtime health for models in production: monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling behavior, and incident response through to root cause and a closed corrective action. Operate the agent runtime layer. Route production agents through the enterprise AI Gateway and MCP Gateway rather than direct model and tool access, migrate existing agents onto that governed path, and keep tool surfaces scoped, versioned and least-privilege as they change. Own evaluation of agents in production, not just before release. Online scoring and behavioral monitoring, quality-regression and drift detection against pinned baselines, sampling and judge pipelines, and the release-gate mechanics that stop a regression from shipping. Build and maintain the observability and evaluation substrate other teams depend on: trace and telemetry capture including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and the data contracts underneath them. Own platform unit economics. Measure and manage cost per inference, per document and per interaction, and produce the platform and infrastructure cost analysis that informs build-versus-buy and hosting decisions. Make the paved road real. Deliver reusable pipeline templates, deployment patterns, reference implementations and internal tooling so product teams adopt the standard path because it is faster, not because it is mandated. Partner with Cloud Engineering, Data Engineering, Security and SRE so the ML platform sits inside enterprise governance, identity and observability rather than beside it. Respond to AI-specific production incidents and drive them to a closed corrective action: prompt injection attempts, rogue-agent cost spikes, data-classification exposure through a tool call, delegation abuse between agents, and model endpoint failures. Maintain the architecture documentation and system diagrams for the ML platform and keep them accurate enough to be used in design review. Mentor engineers and interns on production ML practice and raise the operating standard through design and code review rather than through rework. Competencies: The following items detail how you will be successful in this role. Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field with at least 7 years of relevant experience, or a Master's degree in one of those fields with at least 5 years of relevant experience. 5+ years building and operating production ML or AI systems, with direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting. Demonstrated ownership of a production ML or AI service through its full operating life: deployed it, monitored it, was paged for it, diagnosed a failure, and shipped the fix. Strong Python and SQL, with production-quality engineering practice: version control, testing, code review, and CI/CD applied to ML workloads rather than only to application code. Hands-on experience with a cloud ML platform in production. AWS and Databricks strongly preferred, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow. Working knowledge of how LLM and GenAI workloads differ operationally from traditional ML: token cost and latency behavior, caching and batching, non-deterministic output. Experience running LLM or agent applications behind a gateway or proxy layer, and able to build best practices for model routing and fallback, credential and key management, rate limiting and budget enforcement. Working knowledge of tool-calling architecture for agents, including the Model Context Protocol: what an MCP server is, how tools are scoped and authorized, and why tool access is brokered through a gateway rather than granted directly. Experience with containerization and infrastructure as code. Ability to communicate technical and non-technical trade-offs clearly in writing to an audience that includes both engineers and non-engineers. Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California. Preferred Production experience with GPU-backed model serving, including autoscaling behavior under concurrent load, cold-start management and cost control. Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumenting AI workloads rather than only conventional services. Experience with model serving efficiency techniques: quantization, parameter-efficient fine-tuning, distillation or inference optimization. Experience with data governance and access control on a lakehouse platform, such as Databricks Unity Catalog. Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention and access-control requirements. Experience with agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior. Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and with running MCP servers in a governed environment. Familiarity with emerging agent interoperability and agent identity standards, including Agent2Agent (A2A) style agent-to-agent delegation, agent discovery and capability advertisement, and workload identity for non-human actors. Knowledge and Skills Ability to communicate complex technical information, both verbal and written, to all levels, including senior leadership. Ability to solve problems at the source by offering simple, working solutions. Responds promptly and effectively to resolve incidents, tasks, and projects. Demonstrated ability and motivation to teach others. Ability to gain the trust of others and build solid relationships across and vertically throughout the organization. Effectively prioritize and execute tasks in a high-pressure environment. Target Compensation: This position offers a competitive base salary, with a range of $184,943- $258,920. This position is eligible for an annual variable incentive of cash and equity, between 10- 20% of base salary. Actual amounts are based on individual performance. Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications. This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office. INDENGLP Benefits Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work Our Company Values: To be successful in this role, Team Members need to be: Positive by maintaining resiliency and focusing on solutions Respectful by collaborating and actively listening Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions Direct by effectively communicating and conveying courage Earnest by taking accountability . click apply for full job details

Job Title: AI Architect (GenAI / MLOps) Position Overview Turning promising AI ideas into production systems that are secure, compliant, and built to scale is the hard part of enterprise AI. This role sits at the center of that work. You'll own the architecture for enterprise AI and GenAI solutions end to end, from data foundations and model selection through deployment, governance, and monitoring. You'll work closely with business, data, engineering, and risk teams, and you'll advise executives on what's feasible, what's worth it, and what the trade-offs are. What You'll Do / Key Responsibilities Design end-to-end AI/ML and GenAI architectures (RAG, agents, fine-tuning, model serving) that integrate with existing enterprise platforms Evaluate and select LLMs, vector databases, orchestration frameworks, and MLOps tooling Define reference architectures, patterns, and standards for reuse across teams Partner with data engineering on pipelines, feature stores, and data quality for AI workloads Build in responsible AI: model risk management, explainability, bias testing, audit trails, and regulatory compliance Establish MLOps/LLMOps practices covering CI/CD, evaluation, observability, drift detection, and cost control Lead proofs of concept and pilots, and move them to production Advise stakeholders and executives on AI strategy, feasibility, and trade-offs Mentor engineers and data scientists, and run architecture reviews Required Qualifications 10+ years in software or data engineering, with 4+ years architecting AI/ML solutions in production Hands-on experience with LLMs and GenAI: prompt engineering, RAG, embeddings, and agent frameworks (e.g., LangChain, LlamaIndex) Strong Python skills and fluency in ML frameworks (PyTorch, TensorFlow, scikit-learn) Deep experience with at least one major cloud (AWS, Azure, or GCP) and its AI services (e.g., SageMaker, Bedrock, Azure OpenAI, Vertex AI) Experience with MLOps tooling (MLflow, Kubeflow, Docker, Kubernetes, CI/CD) Solid grounding in data architecture: data lakes/warehouses, streaming, APIs, and microservices Knowledge of security, privacy, and governance for AI systems Excellent communication, with the ability to explain technical trade-offs to non-technical leaders Preferred Qualifications Experience in banking, insurance, or asset/wealth management, including regulatory expectations (e.g., SR 11-7 model risk) Experience with vector databases (Pinecone, pgvector, OpenSearch) and fine-tuning or evaluation frameworks Cloud or architecture certifications (e.g., AWS ML Specialty, Azure AI Engineer, TOGAF) Master's or PhD in Computer Science, Data Science, or a related field What Makes HTC A Great Place To Build Your Future HTC Global Services wants you to join our team. Come build new things with us and advance your career. At HTC Global, you'll collaborate with experts, work alongside clients, and be part of high-performing teams driving success together. You'll have long-term opportunities to grow your career and develop skills in the latest emerging technologies. At HTC Global Services, our employees have access to a comprehensive benefits package. Benefits can include Group Health (Medical, Dental, and Vision), Paid Time Off, Paid Holidays, 401(k) matching, Group Life and Disability insurance, Professional Development opportunities, Wellness programs, and a variety of other perks. Our success as a company is built on inclusion and diversity. HTC Global Services is committed to providing a workplace free from discrimination and harassment, where every employee is treated with dignity and respect. We celebrate differences and believe that diverse cultures, perspectives, and skills drive innovation and success. HTC is an Equal Opportunity Employer and a proud National Minority Supplier. We seek to empower each individual, fostering an environment where everyone feels valued, included, and respected.

Job Description Job Description Dark Wolf Solutions is seeking Security Control Assessor/Representatives (SCA/Rs) to lead security control assessments across high-priority projects. Working at the intersection of cybersecurity engineering, cloud architecture, and DevSecOps prototyping, you will evaluate security controls for cutting-edge AI/LLM technologies across multiple classification levels. This position is ideal for a pragmatic cloud assessor or SCAR who excels in fast-paced DevSecOps environments, understands AWS cloud security, and is eager to shape the cybersecurity posture of next-generation DoD AI capabilities.This position will be based out of Arlington, VA. Additional responsibilities include: Key Responsibilities Execute formal SCA/R duties. Lead security assessment efforts, establishing reusable security playbooks and assessment frameworks for rapid AI deployment into enterprise workflows. Evaluate technical control effectiveness across AWS cloud infrastructure, DevSecOps pipelines, microservices, containerized workloads, and GenAI/LLM application stacks. Partner directly with cybersecurity engineering and DevSecOps prototyping teams to integrate security controls early in the development lifecycle. Review, author, and maintain assessment packages-including System Security Plans (SSPs), Security Assessment Plans (SAPs), Security Assessment Reports (SARs), and POA&Ms-tailored to rapid prototyping and AI systems. Assess technical security risks specific to AI/LLM implementations, such as API exposure, vector database access controls, model integration surface area, and software supply chain dependencies. Support continuous monitoring (ConMon), technical risk evaluations, and cloud architecture reviews across multi-tenant, multi-classification environments. Coordinate with Authorizing Officials (AOs), program managers, and engineering leads to deliver decision-ready risk briefings and ATO recommendations. Provide technical input and oversight for cybersecurity engineering and penetration testing activities across prototype projects. Required Qualifications Active Top Secret security clearance Current DoD 8570/8140 IAM Level II or Level III certification (e.g., Security+, CySA+, CISM, CISSP, CCISO, CAP/CISC) 3-5+ years of experience conducting security control assessments, compliance testing, or A&A/RMF activities for DoD or federal information systems Solid operational understanding of core AWS cloud services (EC2, S3, IAM, VPCs, Security Groups, Security Hub) and how security controls function within cloud-native and CI/CD pipeline environments. Strong working knowledge of NIST SP 800-53 (Rev. 4/5), NIST SP 800-37 (RMF), DoD Cloud Computing SRG, and FedRAMP baselines. Demonstrated experience writing and evaluating core RMF artifacts (SSPs, SAPs, SARs, POA&Ms) Exceptional written and verbal communication skills, with the ability to articulate technical risk clearly to executive stakeholders, Authorizing Officials, and engineering teams. Hands-on experience navigating government GRC repositories, such as eMASS or XACTA. Desired Qualifications Hands-on experience mapping security controls to the NIST AI Risk Management Framework (AI RMF), the OWASP Top 10 for LLM Applications, or the DoD Responsible AI (RAI) Guidelines. Familiarity evaluating secure design patterns for autonomous AI Agents (e.g., tool-calling permissions, sandboxing agent execution environments, prompt boundaries, and ReAct/LangGraph architectures). Experience assessing cloud-managed AI ecosystems and foundation model platforms (e.g., AWS Bedrock, AWS SageMaker, Hugging Face Enterprise, or self-hosted open-source models). Understanding of data protection, access controls, and boundary security for RAG pipelines and vector databases (e.g., OpenSearch Vector Engine, Pinecone, Milvus, or PostgreSQL pgvector). Familiarity evaluating risks unique to LLMs-including prompt injection, data poisoning, model inversion, insecure output handling, and open-source supply chain vulnerabilities in AI libraries (PyTorch, LangChain, LlamaIndex). Exposure to LLM guardrail platforms, evaluation frameworks, or AI security tools (e.g., Promptfoo, Garak, Giskard, NeMo Guardrails) used to test model robustness and output safety. Experience with cATO methodologies, Infrastructure as Code (IaC) templates (Terraform, CloudFormation), and container security (AWS EKS/ECS, Docker). Active AWS Certifications (e.g., AWS Certified Security - Specialty or AWS Certified Solutions Architect). Background or familiarity with offensive security, penetration testing The salary range for this position is estimated to be between $135,000.00 - $150,000.00, commensurate on experience and technical skillset. We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories. In compliance with federal law, all persons hired will be required to verify identity, confirm US Citizenship, and complete the required employment eligibility verification upon hire. We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.

Job Description Job Description Dark Wolf Solutions is seeking Security Control Assessor/Representatives (SCA/Rs) to lead security control assessments across high-priority projects. Working at the intersection of cybersecurity engineering, cloud architecture, and DevSecOps prototyping, you will evaluate security controls for cutting-edge AI/LLM technologies across multiple classification levels. This position is ideal for a pragmatic cloud assessor or SCAR who excels in fast-paced DevSecOps environments, understands AWS cloud security, and is eager to shape the cybersecurity posture of next-generation DoD AI capabilities.This position will be based out of Arlington, VA with hybrid/remote opportunities. Additional responsibilities include: Key Responsibilities Execute formal SCA/R duties. Lead security assessment efforts, establishing reusable security playbooks and assessment frameworks for rapid AI deployment into enterprise workflows. Evaluate technical control effectiveness across AWS cloud infrastructure, DevSecOps pipelines, microservices, containerized workloads, and GenAI/LLM application stacks. Bridge the gap between OffSec and development by applying software design best practices. Lead technical exchange meetings (TEMs), participate in Discovery & Framing workshops, and maintain effective communication with cross-functional teams and stakeholders. Act as the primary subject matter expert and lead developer for custom offensive tooling, integrating disparate capabilities into a cohesive, mission-ready platform. Review, author, and maintain assessment packages-including System Security Plans (SSPs), Security Assessment Plans (SAPs), Security Assessment Reports (SARs), and POA&Ms-tailored to rapid prototyping and AI systems. Assess technical security risks specific to AI/LLM implementations, such as API exposure, vector database access controls, model integration surface area, and software supply chain dependencies. Support continuous monitoring (ConMon), technical risk evaluations, and cloud architecture reviews across multi-tenant, multi-classification environments. Coordinate with Authorizing Officials (AOs), program managers, and engineering leads to deliver decision-ready risk briefings and ATO recommendations. Provide technical input and oversight for cybersecurity engineering and penetration testing activities across prototype projects. Required Qualifications Active Top Secret security clearance Current DoD 8570/8140 IAM Level II or Level III certification (e.g., Security+, CySA+, CISM, CISSP, CCISO, CAP/CISC) 5-7+ years of experience conducting security control assessments, compliance testing, or A&A/RMF activities for DoD or federal information systems Solid operational understanding of core AWS cloud services (EC2, S3, IAM, VPCs, Security Groups, Security Hub) and how security controls function within cloud-native and CI/CD pipeline environments. Experience with GitLab CI/CD (pipeline design, runners, artifact management) and AWS Cloud services (EC2, VPC, IAM, S3). Strong working knowledge of NIST SP 800-53 (Rev. 4/5), NIST SP 800-37 (RMF), DoD Cloud Computing SRG, and FedRAMP baselines. Demonstrated experience writing and evaluating core RMF artifacts (SSPs, SAPs, SARs, POA&Ms) Exceptional written and verbal communication skills, with the ability to articulate technical risk clearly to executive stakeholders, Authorizing Officials, and engineering teams. Hands-on experience navigating government GRC repositories, such as eMASS or XACTA. Desired Qualifications Hands-on experience mapping security controls to the NIST AI Risk Management Framework (AI RMF), the OWASP Top 10 for LLM Applications, or the DoD Responsible AI (RAI) Guidelines. Familiarity evaluating secure design patterns for autonomous AI Agents (e.g., tool-calling permissions, sandboxing agent execution environments, prompt boundaries, and ReAct/LangGraph architectures). Experience assessing cloud-managed AI ecosystems and foundation model platforms (e.g., AWS Bedrock, AWS SageMaker, Hugging Face Enterprise, or self-hosted open-source models). Understanding of data protection, access controls, and boundary security for RAG pipelines and vector databases (e.g., OpenSearch Vector Engine, Pinecone, Milvus, or PostgreSQL pgvector). Familiarity evaluating risks unique to LLMs-including prompt injection, data poisoning, model inversion, insecure output handling, and open-source supply chain vulnerabilities in AI libraries (PyTorch, LangChain, LlamaIndex). Exposure to LLM guardrail platforms, evaluation frameworks, or AI security tools (e.g., Promptfoo, Garak, Giskard, NeMo Guardrails) used to test model robustness and output safety. Experience with cATO methodologies, Infrastructure as Code (IaC) templates (Terraform, CloudFormation), and container security (AWS EKS/ECS, Docker). Active AWS Certifications (e.g., AWS Certified Security - Specialty or AWS Certified Solutions Architect). Background or familiarity with offensive security, penetration testing The salary range for this position is estimated to be between $135,000.00 - $160,000.00, commensurate on experience and technical skillset. We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories. In compliance with federal law, all persons hired will be required to verify identity, confirm US Citizenship, and complete the required employment eligibility verification upon hire. We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.

About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Visa's Technology Organizationis a community of problem solvers and innovators reshaping the future of commerce. We operate the world's most sophisticated processing networks capable of handling more than 65k secure transactions a second across 80M merchants, 15k Financial Institutions, and billions of everyday people. While working with us,you'll get to work on complex distributed systems and solve massive scale problems centered on new payment flows, business and data solutions, cyber security, and B2C platforms. Job Opportunity: We are looking for dedicated, curious, and energetic Software Engineers who embrace solving complex challenges at scale. As a Visa Software Engineer, you will be an integral part of a multi-functional development team inventing, developing, and operating software products and services that help Visa manage a broad portfolio of critical risk programs efficiently at scale. Your work will accelerate Visa business into new markets and sectors and empower and enable multiple productand business groupsacross Visa to deliver innovations faster. Key Responsibilities: Partner with product owners to gather and refine requirements across product teamsto create roadmap, execute and adapt the roadmap in the fast-paced environment. Own Visa's enterprise-wide risk platform that empowers employes to drive organizational and product improvements through collaborative idea sharing and the entire lifecycle from ideation to delivery. Design, implement, and enhance systems and shared technologies end-2-end that enable and improve Visa offerings which touch 40% of the world population while influencing Visa's internal standards for quality, observability, scalability, security, and reusability. Make a difference on a global or local scale through mentorship and continued learning opportunities Use and integrate GenAI and agents in day-to-day activities across SDLC and devops to improve ideation to production delivery cycle time andquality Come up with innovative ideas tointegrate GenAI to enhance and build new product capabilities and agents, and deliver measurable impact to users Drives advanced architect solutions that are robust and scalable, considering integrations with other solutions across the internal technical ecosystem, and external services Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: Master's degree in Math, Science, Engineering, or Computer Science, Information Systems or related field AND minimum five (5) years of experience in a directly related field.ORBachelor's degree in Math, Science, Engineering, or Computer Science, Information Systems or related field AND minimum eight (8) years of experience in a directly related field.ORMinimumeleven(11) years of directly related experience. Preferred Qualifications: Ability to provide technical leadership to their team to successful technical outcomes, ensuring sound engineering decisions that balance business needs along with engineering and operational excellence Strong product mindset and customer focus to contribute meaningfully to planning and collaborate with PMs and team leads to shape team vision. Experience in one or more of the high-level programming languages (e.g., Java, Python, C#, Golang). Experience with SQL and NoSQL databases (e.g., MySQL, Cassandra, MongoDB). Experience with using GenAI, copilots, and agentic software for improving agility, quality, and security in SDLC Proven experience architecting, building, deploying, and operating large scale, highly available, fault-tolerant, and elastic microservices in cloud-native environments. Proficiency in REST/GraphQL/gRPC API design, event-driven architectures, and message queues Familiarity with front-end development with React or Angular Experience building AI-powered product capabilities is a plus Experience in developing and integrating with any Governance, Risk, and Compliance platform is a big plus. Information for US Applicants For roles located in the US, the estimated salary range for this position is $152,200.00 to $ 243,700.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.

Amazon Web Services (AWS) is seeking an experienced Partner Development Manager to own and grow our strategic relationships with global ISVs, driving transformative scale across our Worldwide Public Sector (WWPS) customer base. In this role, you will be the public sector relationship owner between AWS and the partner, responsible for accelerating joint go-to-market motions, driving adoption of AWS services, expanding market penetration, and delivering differentiated customer outcomes across US Federal (Civilian and Defense), State & Local Government, Education, Healthcare, and Nonprofit verticals. Key job responsibilities Own the strategic public sector partner relationship and serve as the primary public sector point of contact, driving joint business planning, executive alignment, and mutual investment across the Worldwide Public Sector segment Define and execute a joint go-to-market strategy focused on accelerating adoption of AWS across public sector customers, with emphasis on mission-critical use cases in Federal (Civilian and Defense), State & Local Government, Education, Healthcare, and Nonprofit verticals Drive co-sell pipeline development and revenue growth - identify, qualify, and advance joint opportunities with public sector end-customers, articulating the differentiated value of AWS infrastructure (including GovCloud and classified environments) Collaborate with AWS field teams, commercial teams, solution architects, and service teams to ensure the partner's models are optimally integrated with AWS services (e.g., Amazon Bedrock, SageMaker, EC2 accelerated compute) and aligned to public sector customer requirements Navigate public sector compliance and security requirements (FedRAMP, IL4/5/6, StateRAMP, ITAR) as they pertain to the partner's model deployment, data residency, and end-customer procurement pathways Maintain an accurate pipeline and forecast of joint GTM opportunities tied to the partner's model release cadence, capability roadmap, and AWS infrastructure dependencies Build and manage executive-level relationships with the partner's public sector, product, and engineering leadership - deliver quarterly business reviews outlining pipeline health, win/loss analysis, adoption metrics, and strategic investment recommendations Serve as the internal voice of the partnership across AWS WWPS leadership, ensuring alignment on prioritization, resource allocation, and escalation paths About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve. BASIC QUALIFICATIONS- 5+ years of developing, negotiating and executing business agreements experience - 5+ years of professional or military experience - 5+ years of working with Core Cloud Technology Services, including, but not limited to Compute, Edge, Hybrid, Security, and/or Networking experience - 5+ years of Go-To-Market, Business Development, Sales, or Consulting experience - 5+ years of working with Enterprise Application Modernization and Migration technologies, including, but not limited to, Mainframe, Serverless, Containers, or Cloud Operations experience - 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience - Bachelor's degree - Experience selling enterprise software or cloud-based applications - Experience explaining complex technical concepts to various business and technical audiences - Experience presenting to both technical and non-technical executive audiences PREFERRED QUALIFICATIONS- Master's degree in business, data science, public administration, finance, engineering, human resources, or related field, or PMP certificate - Experience interpreting data and making business recommendations - Experience identifying, negotiating, and executing complex legal agreements - Experience interpreting data and making business recommendations across leadership and cross-functional teams Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at . USA, VA, Arlington - 147 100.00 USD annually

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Job Description Job Description Dark Wolf Solutions is seeking Security Control Assessor/Representatives (SCA/Rs) to lead security control assessments across high-priority projects. Working at the intersection of cybersecurity engineering, cloud architecture, and DevSecOps prototyping, you will evaluate security controls for cutting-edge AI/LLM technologies across multiple classification levels. This position is ideal for a pragmatic cloud assessor or SCAR who excels in fast-paced DevSecOps environments, understands AWS cloud security, and is eager to shape the cybersecurity posture of next-generation DoD AI capabilities.This position will be based out of Arlington, VA. Additional responsibilities include: Key Responsibilities Execute formal SCA/R duties. Lead security assessment efforts, establishing reusable security playbooks and assessment frameworks for rapid AI deployment into enterprise workflows. Evaluate technical control effectiveness across AWS cloud infrastructure, DevSecOps pipelines, microservices, containerized workloads, and GenAI/LLM application stacks. Partner directly with cybersecurity engineering and DevSecOps prototyping teams to integrate security controls early in the development lifecycle. Review, author, and maintain assessment packages-including System Security Plans (SSPs), Security Assessment Plans (SAPs), Security Assessment Reports (SARs), and POA&Ms-tailored to rapid prototyping and AI systems. Assess technical security risks specific to AI/LLM implementations, such as API exposure, vector database access controls, model integration surface area, and software supply chain dependencies. Support continuous monitoring (ConMon), technical risk evaluations, and cloud architecture reviews across multi-tenant, multi-classification environments. Coordinate with Authorizing Officials (AOs), program managers, and engineering leads to deliver decision-ready risk briefings and ATO recommendations. Provide technical input and oversight for cybersecurity engineering and penetration testing activities across prototype projects. Required Qualifications Active Top Secret security clearance Current DoD 8570/8140 IAM Level II or Level III certification (e.g., Security+, CySA+, CISM, CISSP, CCISO, CAP/CISC) 3-5+ years of experience conducting security control assessments, compliance testing, or A&A/RMF activities for DoD or federal information systems Solid operational understanding of core AWS cloud services (EC2, S3, IAM, VPCs, Security Groups, Security Hub) and how security controls function within cloud-native and CI/CD pipeline environments. Strong working knowledge of NIST SP 800-53 (Rev. 4/5), NIST SP 800-37 (RMF), DoD Cloud Computing SRG, and FedRAMP baselines. Demonstrated experience writing and evaluating core RMF artifacts (SSPs, SAPs, SARs, POA&Ms) Exceptional written and verbal communication skills, with the ability to articulate technical risk clearly to executive stakeholders, Authorizing Officials, and engineering teams. Hands-on experience navigating government GRC repositories, such as eMASS or XACTA. Desired Qualifications Hands-on experience mapping security controls to the NIST AI Risk Management Framework (AI RMF), the OWASP Top 10 for LLM Applications, or the DoD Responsible AI (RAI) Guidelines. Familiarity evaluating secure design patterns for autonomous AI Agents (e.g., tool-calling permissions, sandboxing agent execution environments, prompt boundaries, and ReAct/LangGraph architectures). Experience assessing cloud-managed AI ecosystems and foundation model platforms (e.g., AWS Bedrock, AWS SageMaker, Hugging Face Enterprise, or self-hosted open-source models). Understanding of data protection, access controls, and boundary security for RAG pipelines and vector databases (e.g., OpenSearch Vector Engine, Pinecone, Milvus, or PostgreSQL pgvector). Familiarity evaluating risks unique to LLMs-including prompt injection, data poisoning, model inversion, insecure output handling, and open-source supply chain vulnerabilities in AI libraries (PyTorch, LangChain, LlamaIndex). Exposure to LLM guardrail platforms, evaluation frameworks, or AI security tools (e.g., Promptfoo, Garak, Giskard, NeMo Guardrails) used to test model robustness and output safety. Experience with cATO methodologies, Infrastructure as Code (IaC) templates (Terraform, CloudFormation), and container security (AWS EKS/ECS, Docker). Active AWS Certifications (e.g., AWS Certified Security - Specialty or AWS Certified Solutions Architect). Background or familiarity with offensive security, penetration testing The salary range for this position is estimated to be between $135,000.00 - $150,000.00, commensurate on experience and technical skillset. We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories. In compliance with federal law, all persons hired will be required to verify identity, confirm US Citizenship, and complete the required employment eligibility verification upon hire. We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. Enterprise Platforms Technology (EPTech) comprises many of Capital One's most important enterprise platforms. We play an essential role in establishing practices for building technology solutions across the company, while also delivering capabilities that exemplify those practices. Team: The Marketing and Messaging team is responsible for delivering hyper-personalized messages and experiences that will delight the customer, attract prospects and drive increasing business value. The team builds scalable platforms that deliver omnichannel messages in owned and paid Adtech channels. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 Plano, TX: $179,400 - $204,700 for Machine Learning Engineer 4 Richmond, VA: $179,400 - $204,700 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Job Description Job Description Dark Wolf Solutions is seeking Security Control Assessor/Representatives (SCA/Rs) to lead security control assessments across high-priority projects. Working at the intersection of cybersecurity engineering, cloud architecture, and DevSecOps prototyping, you will evaluate security controls for cutting-edge AI/LLM technologies across multiple classification levels. This position is ideal for a pragmatic cloud assessor or SCAR who excels in fast-paced DevSecOps environments, understands AWS cloud security, and is eager to shape the cybersecurity posture of next-generation DoD AI capabilities.This position will be based out of Arlington, VA with hybrid/remote opportunities. Additional responsibilities include: Key Responsibilities Execute formal SCA/R duties. Lead security assessment efforts, establishing reusable security playbooks and assessment frameworks for rapid AI deployment into enterprise workflows. Evaluate technical control effectiveness across AWS cloud infrastructure, DevSecOps pipelines, microservices, containerized workloads, and GenAI/LLM application stacks. Bridge the gap between OffSec and development by applying software design best practices. Lead technical exchange meetings (TEMs), participate in Discovery & Framing workshops, and maintain effective communication with cross-functional teams and stakeholders. Act as the primary subject matter expert and lead developer for custom offensive tooling, integrating disparate capabilities into a cohesive, mission-ready platform. Review, author, and maintain assessment packages-including System Security Plans (SSPs), Security Assessment Plans (SAPs), Security Assessment Reports (SARs), and POA&Ms-tailored to rapid prototyping and AI systems. Assess technical security risks specific to AI/LLM implementations, such as API exposure, vector database access controls, model integration surface area, and software supply chain dependencies. Support continuous monitoring (ConMon), technical risk evaluations, and cloud architecture reviews across multi-tenant, multi-classification environments. Coordinate with Authorizing Officials (AOs), program managers, and engineering leads to deliver decision-ready risk briefings and ATO recommendations. Provide technical input and oversight for cybersecurity engineering and penetration testing activities across prototype projects. Required Qualifications Active Top Secret security clearance Current DoD 8570/8140 IAM Level II or Level III certification (e.g., Security+, CySA+, CISM, CISSP, CCISO, CAP/CISC) 5-7+ years of experience conducting security control assessments, compliance testing, or A&A/RMF activities for DoD or federal information systems Solid operational understanding of core AWS cloud services (EC2, S3, IAM, VPCs, Security Groups, Security Hub) and how security controls function within cloud-native and CI/CD pipeline environments. Experience with GitLab CI/CD (pipeline design, runners, artifact management) and AWS Cloud services (EC2, VPC, IAM, S3). Strong working knowledge of NIST SP 800-53 (Rev. 4/5), NIST SP 800-37 (RMF), DoD Cloud Computing SRG, and FedRAMP baselines. Demonstrated experience writing and evaluating core RMF artifacts (SSPs, SAPs, SARs, POA&Ms) Exceptional written and verbal communication skills, with the ability to articulate technical risk clearly to executive stakeholders, Authorizing Officials, and engineering teams. Hands-on experience navigating government GRC repositories, such as eMASS or XACTA. Desired Qualifications Hands-on experience mapping security controls to the NIST AI Risk Management Framework (AI RMF), the OWASP Top 10 for LLM Applications, or the DoD Responsible AI (RAI) Guidelines. Familiarity evaluating secure design patterns for autonomous AI Agents (e.g., tool-calling permissions, sandboxing agent execution environments, prompt boundaries, and ReAct/LangGraph architectures). Experience assessing cloud-managed AI ecosystems and foundation model platforms (e.g., AWS Bedrock, AWS SageMaker, Hugging Face Enterprise, or self-hosted open-source models). Understanding of data protection, access controls, and boundary security for RAG pipelines and vector databases (e.g., OpenSearch Vector Engine, Pinecone, Milvus, or PostgreSQL pgvector). Familiarity evaluating risks unique to LLMs-including prompt injection, data poisoning, model inversion, insecure output handling, and open-source supply chain vulnerabilities in AI libraries (PyTorch, LangChain, LlamaIndex). Exposure to LLM guardrail platforms, evaluation frameworks, or AI security tools (e.g., Promptfoo, Garak, Giskard, NeMo Guardrails) used to test model robustness and output safety. Experience with cATO methodologies, Infrastructure as Code (IaC) templates (Terraform, CloudFormation), and container security (AWS EKS/ECS, Docker). Active AWS Certifications (e.g., AWS Certified Security - Specialty or AWS Certified Solutions Architect). Background or familiarity with offensive security, penetration testing The salary range for this position is estimated to be between $135,000.00 - $160,000.00, commensurate on experience and technical skillset. We are proud to be an EEO/AA employer Minorities/Women/Veterans/Disabled and other protected categories. In compliance with federal law, all persons hired will be required to verify identity, confirm US Citizenship, and complete the required employment eligibility verification upon hire. We are strictly looking for direct, full-time W2 employees. We do not engage with third-party staffing agencies, C2C, or 1099 independent contractors for this role.